Pass the required Refstack tests for the capabilities matrix in the version of OpenStack that they use. Vendors are expected (not required) to share their results.

Run and include the “designated sections” of code for the OpenStack components that you include.

Other basic obligations in their license agreement like being a currently paid up corporate sponsor or foundation member, etc.

If they meet these conditions, vendors can use the OpenStack mark in their product names and descriptions.

Enough preamble! Let’s see the three Advisory Cases

MANDATORY DISCLAIMER: These conditions apply to fictional public, private and client use cases. Any resemblence to actual companies is a function of the need to describe real use-cases. These cases are advisory for illustration use only and are not to be considered definitive guidenance because DefCore is still evolving.

Public Cloud: Service Provider “BananaCloud”

A popular public cloud operator, BananaCloud has been offering OpenStack-based IaaS since the Diablo release. However, they don’t use the Keystone component. Since they also offer traditional colocation and managed services, they have an existing identity management system that they use. They made a similar choice for Horizon in favor of their own cloud portal.

They use Nova a custom scheduler and pass all the Nova tests. This is the simplest case since they use code and pass the tests.

In the Havana DCCM, the Keystone capabilities are a must pass test; however, there are no designated sections of code for Keystone. So BananaCloud must implement a Keystone-compatible API on their IaaS environment (an effort they had underway already) that will pass Refstack, and they’re good to go.

There are no must pass tests for Horizon so they have no requirements to include those features or code. They can still be OpenStack without Horizon.

There are no must pass tests for Trove so they have no brand requirements to include those features or code so it’s not a brand issue; however, by using Trove and promoting its use, they increase the likelihood of its capabilities becoming must pass features.

BananaCloud also offers some advanced OpenStack capabilities, including Marconi and Trove. Since there are no must pass capabilities from these components in the Havana DCCM, it has no impact on their offering additional services. DefCore defines the minimum requirements and encourages vendors to share their full test results of additional capabilities because that is how OpenStack identifies new must pass candidates.

Note: The DefCore DCCM is advisory for the Havana release, so if BananaCloud is late getting their Keystone-compatibility work done there won’t be any commercial impact. But it will be a binding part of the trademark license agreement by the Juno release, which is only 6 months away.

Private Cloud: SpRocket Small-Business OpenStack Software

SpRocket is a new OpenStack software vendor, specializing in selling a Windows-powered version of OpenStack with tight integration to Sharepoint and AzurePack. In their feature set, they only need part of Nova and provide an alternative object storage to Swift that implements a version of the Swift API. They do use Heat as part of their implementation to set up applications back ended by Sharepoint and AzurePack.

For Nova, they already use the code and have already implemented all required capabilities except for the key-store. To comply with the DefCore requirement, they must enable the key-store capability.

While their implementation of Swift passes the tests, We are still working to resolve the final disposition of Swift so there are several possible outcomes:

If Swift is 0% designated then they are OK (that’s illustrated here)

If Swift is 100% designated then they cannot claim to be OpenStack.

If Swift is partially designated then they have to adapt their deploy to include the required code.

Their use of Heat is encouraged since it is an integrated project; however, there are no required capabilities and does not influence their ability to use the mark.

They use the trunk version of Windows HyperV drivers which are not designated and have no specific tests.

Ecosystem Client: “Mist” OpenStack-consuming Client Library

Mist is a client library for load+kt programmers working on applications using the OpenStack APIs. While it’s an open source project, there are many commercial applications that use the library for their applications. Unlike a “pure” OpenStack program, it also supports other Cloud APIs.

Since the Mist library does not ship or implement the OpenStack code base, the DefCore process does not apply to their effort; however, there are several important intersections with Mist and OpenStack and Core.

First, it is very important for the DefCore process that Mist map their use of the OpenStack APIs to the capabilities matrix. They are asked to help with this process because they are the best group to answer the “works with clients” criteria.

Second, if there are APIs used by Mist that are not currently tested then the OpenStack community should work with the Mist community to close those test gaps.

Third, if Mist relies on an API that is not must-pass they are encouraged to help identify those capabilities as core candidates in the community.

I knew from the start that we had too little time and, thankfully, people were tolerant of my need to stop the discussions. In the end, we were able to cover all the planned topics. This was important because all these features are interlocked so discussions were iterative. I was impressed with the level of knowledge at the table and it drove deep discussion. Even so, there are still parts of Crowbar that are confusing (networking, late binding, orchestration, chef coupling) even to collaborators.

In typing up these notes, it becomes even more blindingly obvious that the core features for Crowbar 2 are highly interconnected. That’s no surprise technically; however, it will make the notes harder to follow because of knowledge bootstrapping. You need take time and grok the gestalt and surf the zeitgeist.

Collaboration Invitation: I wanted to remind readers that this summit was just the kick-off for a series of open weekly design (Tuesdays 10am CDT) and coordination (Thursdays 8am CDT) meetings. Everyone is welcome to join in those meetings – information is posted, recorded, folded, spindled and mutilated on the Crowbar 2 wiki page.

These notes are my reflection of the online etherpad notes that were made live during the meeting. I’ve grouped them by design topic.

Due to time limits, we have to stop discussions and continue them online.

We are hoping to align Crowbar 2 beta and OpenStack Folsom release.

Online / Connected Mode

Online mode is more than simply internet connectivity. It is the foundation of how Crowbar stages dependencies and components for deploy. It’s required for heterogeneous O/S, pull from source and it has dependencies on how we model networking so nodes can access resources.

We are thinking to use caching proxies to stage resources. This would allow isolated production environments and preserves the run everything from ISO without a connection (that is still a key requirement to us).

Suse’s Crowbar fork does not build an ISO, instead it relies on RPM packages for barclamps and their dependencies.

Pulling packages directly from the Internet has proven to be unreliable, this method cannot rely on that alone.

Install From Source

This feature is mainly focused on OpenStack, it could be applied more generally. The principals that we are looking at could be applied to any application were the source code is changing quickly (all of them?!). Hadoop is an obvious second candidate.

We spent some time reviewing the use-cases for this feature. While this appears to be very dev and pre-release focused, there are important applications for production. Specifically, we expect that scale customers will need to run ahead of or slightly adjacent to trunk due to patches or proprietary code. In both cases, it is important that users can deploy from their repository.

We discussed briefly our objective to pull configuration from upstream (not just OpenStack, but potentially any common cookbooks/modules). This topic is central to the CMDB agnostic discussion below.

The overall sentiment is that this could be a very powerful capability if we can manage to make it work. There is a substantial challenge in tracking dependencies – current RPMs and Debs do a good job of this and other configuration steps beyond just the bits. Replicating that functionality is the real obstacle.

CMDB agnostic (decoupling Chef)

This feature is confusing because we are not eliminating the need for a configuration management database (CMDB) tool like Chef, instead we are decoupling Crowbar from the a single CMDB to a pluggable model using an abstraction layer.

It was stressed that Crowbar does orchestration – we do not rely on convergence over multiple passes to get the configuration correct.

We had strong agreement that the modules should not be tightly coupled but did need a consistent way (API? Consistent namespace? Pixie dust?) to share data between each other. Our priority is to maintain loose coupling and follow integration by convention and best practices rather than rigid structures.

The abstraction layer needs to have both import and export functions

Crowbar will use attribute injection so that Cookbooks can leverage Crowbar but will not require Crowbar to operate. Crowbar’s database will provide the links between the nodes instead of having to wedge it into the CMDB.

In 1.x, the networking was the most coupled into Chef. This is a major part of the refactor and modeling for Crowbar’s database.

There are a lot of notes captured about this on the etherpad – I recommend reviewing them

Heterogeneous OS (bare metal provisioning and beyond)

This topic was the most divergent of all our topics because most of the participants were using some variant of their own bare metal provisioning project (check the etherpad for the list).

Since we can’t pack an unlimited set of stuff on the ISO, this feature requires online mode.

Most of these projects do nothing beyond OS provisioning; however, their simplicity is beneficial. Crowbar needs to consider users who just want a stream-lined OS provisioning experience.

The Rackspace RA and Crowbar deployment barclamps share the same objective: sharing of best practices for OpenStack operations.

Over the last 12+ months, my team at Dell has had the opportunity to work with many customers on OpenStack deployment designs. While no two of these are identical, they do share many similarities. We are pleased to collaborate with Rackspace and others on capturing these practices as operational code (or “opscode” if you want a reference to the Chef cookbooks that are an intrinsic part of Crowbar’s architecture).

In our customer interactions, we hear clearly that Crowbar must remain flexible and ready to adapt to both customer on-site requirements and evolution within the OpenStack code base. You are also telling us that there is a broader application space for Crowbar and we are listening to that too.

I believe that it will take some time for the community and markets to process today’s Rackspace announcements. Rackspace is showing strong leadership in both sharing information and commercialization around OpenStack. Both of these actions will drive responses from the community members.

With the OpenStack launch behind me, I have some time to attend the Cloud Connect Event. I missed all the DevOps sessions, but was getting to geek out on the NoSQL & Big Data sessions. I jumped to the private cloud track (based on Twitter traffic) and was rewarded for the shift.

I’m surprised at how much focus this cloud conference is dedicated to private cloud. At other cloud conferences I’ve attended, the focus has been on learning how to use the cloud (specifically the public cloud). This is the first cloud show I’ve attended that has so much emphasis, dialog and vendor feeding around private. This was a suits & slacks show with few jeans, t-shirts, and pony tails. Perhaps private cloud is where the $$$ is being spent now?

It definitely feels like using cloud has become assumed, but the best practices and tools are just emerging.

The twitter #ccevent stream is interesting but temporal. I’m posting my raw (spelling optional) notes (below the more tag) because there is a lot of great content from the show to support and extend the twitter stream. I’ll try to italicize some of the better lines.

This post is the final post laying out a rethinking of how we view user and buyer motivations for public and private clouds.

In part 1, I laid out the “magic cube” that showed a more discrete technological breakdown of cloud deployments (see that for the MSH, MDH, MDO, UDO key). In part 2, I piled higher and deeper business vectors onto the cube showing that the cost value of the vertices was not linear. The costs were so unequal that they pulled our nice isometric cube into a cone.

The Cloud Gravity Well

To help make sense of cloud gravity, I’m adding a qualitative measure of friction.

Friction represents the cloud consumer’s willingness to adopt the requirements of our cloud vertices. I commonly hear people say they are not willing to put sensitive data “in the cloud” or they are worried about a “lack of security.” These practical concerns create significant friction against cloud adoption; meanwhile, being able to just “throw up” servers (yuck!) and avoiding IT restrictions make it easy (low friction) to use clouds.

Historically, it was easy to plot friction vs. cost. There was a nice linear trend where providers simply lowered cost to overcome friction. This has been fueling the current cloud boom.

The magic cube analysis shows another dynamic emerging because of competing drivers from management and isolation. The dramatic saving from outsource management are inhibited by the high friction for giving up data protection, isolation, control, and performance minimums. I believe that my figure, while pretty, dramaticallyunderstates the friction gap between dedicated and shared hosting. This tension creates a non-linear trend in which substantial customer traction will follow the more expensive offerings. In fact, it may be impossible to overcome this friction with pricing pressure.

I believe this analysis shows that there’s a significant market opportunity for clouds that have dedicated resources yet are still managed and hosted by a trusted 3rd party. On the other hand, this gravity well could turn out to be a black hole money pit. Like all cloud revolutions, the timid need not apply.

Post Script: Like any marketing trend, there must be a name. These clouds are not “private” in the conventional sense and I cringe at using “hybrid” for anything anymore. If current public clouds are like hotels (or hostels) then these clouds are more like condos or managed community McMansions. I think calling them “cloud shards” is very crisp, but my marketing crystal ball says “try again.” Suggestions?

In part 1 of this series, I laid out the “magic cube” that describes 8 combinations for cloud deployment. The cube provides a finer grain understanding than “public” vs. “private” clouds because we can now clearly see that there are multiple technology axis that create “private IT” that can be differentiated.

In this section, we take off our technologist pocket protectors and pick up our finance abacus. The next level of understanding for the magic cube is to translate the technology axis into business vectors. The business vectors are:

X. Capitalization: OpEx vs. CapEx. On the surface, this determines who has ownership of resource, but the deeper issue is if the resource is an investment (capital) or consumable (operations). Unless you’re talking about a co-lo cage, hosting models will be consumable because the host is leveraging (in the financial sense) their capital into operating revenue.

Y. Commitment: PayGo vs. Fixed. Like a cell phone plan, you can pay for what you use (Pay-as-you-go) or lock-in to a fixed contract. Fixed is generally pays a premium based on volume even though the per unit cost may be lower. In my thinking, the fixed contract may include dedicated resource guarantees and additional privacy.

Z. Management: Insource vs. Outsource. Don’t over think this vector, but remember I not talking about off shoring! If you are directly paying people to manage your cloud then you’ve insourced management. If the host provides services, process or automation that reduces hiring requirements then you’re outsourcing it IT. It’s critical to realize that you can’t employee fractional people. There are fundamental cloud skillsets and tools that must be provided to operate a cloud (including, not limited to DevOps).

THE 3 VECTORS ARE NOT EQUAL!

If you were willing to do some cerebral calisthenics about these vectors then you realized that they are not equal cost weights. Let’s look at them from least to most.

The commitment vector is very easy to traverse this vector in either direction. It’s well established human behavior that we’ll pay more for to be more predictable, especially if that means we get more control or privacy. If I had had a dollar for everyone who swoons over cloud bursting I’d go buy that personal jet pack.

The capitalization vector has is part of the driver to cloud as companies (and individuals) seek to avoid buying servers up front. It also helps that clouds let you buy factional servers and “throw away” servers that you don’t need. While these OpEx aspects of cloud are nice, servers are really not that expensive to lease or idle. Frankly, it’s the deployment and management of those assets that drives the TCO way up for CapEx clouds, but that’s not this vector so move along.

The management vector is the silverback gorilla standing in the corner of our magic cube. Acquiring and maintaining the operations expertise is a significant ongoing expense. In many cases, companies simply cannot afford to adequately cover the needed skills and this severely limits their ability to use technology competitively. Hosts are much better positioned to manage cloud infrastructure because they enjoy economies of scale distributed between multiple customers. This vector is heavily one directional – once you fly without that critical Ops employee in favor of a host doing the work, it is unlikely you’ll hire that role.

The unequal cost weights pull our cube out of shape. They create a strong customer pull away from the self-managed & CapEx vertices and towards outsourced & OpEx. I think of this distortion as a cloud gravity well that pulls customers down from private into public clouds.

That’s enough for today. You’ll have to wait for the gravity well analysis in part 3.